Prasad Keerthana, Zimmermann Bernhard, Prabhu Gopalakrishna, Pai Muktha
Manipal Centre for Information Science, Manipal University, Manipal 576104, India.
Open Med Inform J. 2010 May 28;4:86-93. doi: 10.2174/1874431101004010086.
Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at http://tma.stanford.edu/cgi-bin/cx?n=her1, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92.31% sensitivity and 93.18% specificity for ER expression image classification and 96.67% sensitivity and 88.24% specificity for HER-2/neu expression image classification.
乳腺癌是人类第二常见的肿瘤，在女性所有癌症中约占四分之一，仅次于宫颈癌。雌激素受体（ER）、孕激素受体和人表皮生长因子受体（HER-2/neu）的表达在乳腺癌的诊断和预后中起着重要作用。组织微阵列（TMA）技术是一种高通量技术，可提供一组经过均匀染色的标准化图像，便于对标本图像进行有效的自动化评估。TMA技术广泛用于评估激素表达以诊断乳腺癌。如果考虑组织微阵列过程工作流程中每个步骤所花费的时间，可以发现分析步骤花费的时间最多。因此，自动化分析将显著减少完成研究所需的总时间。有许多工具可用于从微阵列载玻片自动数字采集斑点图像。这些图像中的每一张都需要由病理学家进行评估，以根据染色强度分配一个分数来代表激素表达，将它们分类为阴性或阳性病例。我们的工作旨在开发一个系统，用于自动评估通过组织微阵列技术生成的代表ER表达图像和HER-2/neu表达图像的图像集。我们的研究基于斯坦福大学的组织微阵列数据库门户http://tma.stanford.edu/cgi-bin/cx?n=her1，该门户为研究人员提供了大量图像。我们使用了171张与乳腺癌ER表达对应的图像和214张与HER-2/neu表达对应的图像。在与ER表达对应的171张图像中，104张为阴性，67张为阳性病例。在与HER-2/neu表达对应的214张图像中，112张为阴性，102张为阳性病例。我们的方法对ER表达图像分类的灵敏度为92.31%，特异性为93.18%；对HER-2/neu表达图像分类的灵敏度为96.67%，特异性为88.24%。
